PeekMed web

K240926

Peek Health, S.A. · cleared 2024-12-06 · product code LLZ · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
PeekMed web is a system designed to help healthcare professionals carry out pre-operative planning for several surgical procedures, based on their imported patients' imaging studies. Experience in usage and a clinical assessment is necessary for the proper use of the system in the revision and approval of the output of the planning. The multi-platform system works with a database of digital representations related to surgical materials supplied by their manufacturers. As PeekMed web is capable of representing medical images in a 2D or 3D environment, performing relevant measurements on those images, and also capable of adding templates, it then can perform a total overview of the surgery. Being software it does not interact with any part of the body of the user and/or patient.
AlgorithmML model variants for bone segmentation, landmarking, and classification
source quote (p.9)
The new variants of the ML model for foot are considered in this Traditional 510(k) and were developed according to what is defined for each ML model development and validation according to their intended requirements and performance. The subject device includes new and updated ML model variants for the purpose of bone segmentation. The subject device includes new and updated ML model variants for the purpose of landmarking. ML models incorporated into PeekMed web were also developed, trained, tested, and externally validated for their performance according to the internal procedures.
Adaptive (vs locked)No
source quote (p.15)
ML models incorporated into PeekMed web were also developed, trained, tested, and externally validated for their performance according to the internal procedures. O A dedicated validation dataset containing different data from the ML development dataset was used. Specifically, the validation dataset was not a sampling of the development dataset, has never been used for the algorithm training or for tunning the algorithm, and leakage between development and validation data sets did not occur.
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (2)

Bench

sample size not stated

Retrospective clinical

n=367 cases

endpoints: DICE is no less than 90%; HD-95 is no more than 8; STD DICE is between +/- 10%; Precision is more than 85%; Recall is more than 90%; MRE is no more than 7mm; STD MRE is between +/- 5mm; Accuracy is no less than 90%; Precision is no less than 85%; Recall is no less than 90%; F1 score is no less than 90%

Reported performance (8 observations)

diceas written: “DICEstated without valueCI no less than 90%
source quote (p.16)
DICE is no less than 90%
diceas written: “STD DICEstated without valueCI between +/- 10%
source quote (p.16)
STD DICE is between +/- 10%
ppvas written: “Precision (Segmentation)stated without valueCI more than 85%
source quote (p.16)
Precision is more than 85%
sensitivityas written: “Recall (Segmentation)stated without valueCI more than 90%
source quote (p.16)
Recall is more than 90%
accuracyas written: “Accuracy (Classification)stated without valueCI no less than 90%
source quote (p.16)
Accuracy is no less than 90%.
ppvas written: “Precision (Classification)stated without valueCI no less than 85%
source quote (p.16)
Precision is no less than 85%
sensitivityas written: “Recall (Classification)stated without valueCI no less than 90%
source quote (p.16)
Recall is no less than 90%
f1as written: “F1 score (Classification)stated without valueCI no less than 90%
source quote (p.16)
F1 score is no less than 90%

Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.

Predicate network

Postmarket — what happened after clearance

48
recalls in product code, 24mo
295
MAUDE reports in code, 12mo
+683%
vs code's own 3-yr baseline
30
drift signals on this device
  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K252856 (decision 2025-12-22) from Peek Health, S.A. for a matching device line ("PeekMed web") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K252856

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K252452 (decision 2025-11-12) from Peek Health, S.A. for a matching device line ("PeekMed web") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K252452

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K251096 (decision 2025-07-14) from Peek Health, S.A. for a matching device line ("PeekMed web") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K251096

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K250042 (decision 2025-03-19) from Peek Health, S.A. for a matching device line ("PeekMed web") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K250042

  • adverse_event_inflection

    MAUDE adverse-event reports for product code LLZ: 295 in the 12 months ending 2026-06, vs a 37.7/12mo average over the prior 3 windows (+683%). Code-level count — reports are not attributed to this specific device.

    first seen 2026-07-08 · openFDA /device/event.json count=date_received product_code=LLZ

  • recall_reason_pattern

    Software/algorithm-related recall in product code LLZ (GE Medical Systems SCS, initiated 2026-05-08): "GE HealthCare has become aware of a context synchronization issue in AW Server 3.2 ext. 6.5. When a user selects a patient or exam in the AW Server Web Client worklist and launches" Recalling firm is another firm in the same product code.

    first seen 2026-07-08 · recall res_event_number:99042

  • …and 24 more.

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.

Reimbursement — how devices like this got paid

Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).

RIGOR™ Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: radar.healthai.com/precedent/device/K240926